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        <h2 id="机器学习主要术语"><a href="#机器学习主要术语" class="headerlink" title="机器学习主要术语"></a>机器学习主要术语</h2><h3 id="1-监督式机器学习"><a href="#1-监督式机器学习" class="headerlink" title="1.监督式机器学习"></a>1.监督式机器学习</h3><blockquote>
<p>机器学习系统通过学习如何组合输入信息来对从未见过的数据做出有用的预测。</p>
</blockquote>
<h3 id="2-标签"><a href="#2-标签" class="headerlink" title="2.标签"></a>2.标签</h3><blockquote>
<p><strong>标签</strong>是我们要预测的事物，即简单线性回归中的 y 变量。标签可以是小麦未来的价格、图片中显示的动物品种、音频剪辑中的含义或任何事物。</p>
</blockquote>
<h3 id="3-特征"><a href="#3-特征" class="headerlink" title="3.特征"></a>3.特征</h3><blockquote>
<p><strong>特征</strong>是输入变量，即简单线性回归中的 x 变量。简单的机器学习项目可能会使用单个特征，而比较复杂的机器学习项目可能会使用数百万的特征，按如下方式指定：<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">&#123;x1, x2, ... xn&#125;</span><br></pre></td></tr></table></figure></p>
</blockquote>
<h3 id="4-样本"><a href="#4-样本" class="headerlink" title="4.样本"></a>4.样本</h3><blockquote>
<p>样本是指数据的特定实例：<strong>x</strong>。（采用粗体<strong>x</strong>表示它是一个矢量。）我们将样本分为以下两类：</p>
<ul>
<li>有标签样本</li>
<li>无标签样本</li>
</ul>
</blockquote>
<p>有标签样本同时包括特征和标签。即：<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">labeled examples: &#123;features, label&#125;: (x, y)</span><br></pre></td></tr></table></figure></p>
<p>无标签样本包含特征，但不包含标签。即：<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">unlabeled examples: &#123;features, ?&#125;: (x, ?)</span><br></pre></td></tr></table></figure></p>
<p>在使用有标签样本训练了我们德 模型之后，我们会使用该模型来预测无标签样本的标签。</p>
<h3 id="5-模型"><a href="#5-模型" class="headerlink" title="5.模型"></a>5.模型</h3><blockquote>
<p>模型定义了特征与标签之间的关系。模型生命周期的冷两个阶段：</p>
<ul>
<li><strong>训练</strong>表示创建或学习模型。也就是说，向模型展示有标签样本，让模型逐渐学习特征与标签之间的关系。</li>
<li><strong>推断</strong>表示将训练后的模型应用于无标签的样本。也就是说，使用训练后的模型来做出有用的预测。</li>
</ul>
</blockquote>
<h3 id="6-回归于分类"><a href="#6-回归于分类" class="headerlink" title="6.回归于分类"></a>6.回归于分类</h3><ul>
<li><strong>回归</strong>模型可预测连续值</li>
<li><strong>分类</strong>模型可预测离散值</li>
</ul>
<h2 id="训练与损失"><a href="#训练与损失" class="headerlink" title="训练与损失"></a>训练与损失</h2><p>简单来说，<strong>训练</strong>模型表示通过让有标签样本来学习（确定）所有权重和偏差的理想值。在监督式学习中，机器学习算法通过以下方式构建模型：检查多个样本并尝试找出可最大限度地减少损失的模型；这一过程称为<strong>经验风险最小化</strong>。</p>
<p>损失是对糟糕预测的惩罚。也就是说，损失是一个数值，表示对于单个样本而言模型预测的准确程度。如果模型的预测完全准确，则损失为零，否则损失会较大。训练模型的目标是从所有样本中找到一组平均损失“较小”的权重和偏差。</p>
<h3 id="平均损失（L2损失）"><a href="#平均损失（L2损失）" class="headerlink" title="平均损失（L2损失）"></a>平均损失（L2损失）</h3><blockquote>
<p>(observation - prediction(<strong>x</strong>))^2 = (y - y’)2</p>
</blockquote>
<h3 id="均方损失（MSE）"><a href="#均方损失（MSE）" class="headerlink" title="均方损失（MSE）"></a>均方损失（MSE）</h3><blockquote>
<p>每个样本的平均平方损失</p>
</blockquote>

      
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              <div class="post-toc-content"><ol class="nav"><li class="nav-item nav-level-2"><a class="nav-link" href="#机器学习主要术语"><span class="nav-number">1.</span> <span class="nav-text">机器学习主要术语</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#1-监督式机器学习"><span class="nav-number">1.1.</span> <span class="nav-text">1.监督式机器学习</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#2-标签"><span class="nav-number">1.2.</span> <span class="nav-text">2.标签</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#3-特征"><span class="nav-number">1.3.</span> <span class="nav-text">3.特征</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#4-样本"><span class="nav-number">1.4.</span> <span class="nav-text">4.样本</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#5-模型"><span class="nav-number">1.5.</span> <span class="nav-text">5.模型</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#6-回归于分类"><span class="nav-number">1.6.</span> <span class="nav-text">6.回归于分类</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#训练与损失"><span class="nav-number">2.</span> <span class="nav-text">训练与损失</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#平均损失（L2损失）"><span class="nav-number">2.1.</span> <span class="nav-text">平均损失（L2损失）</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#均方损失（MSE）"><span class="nav-number">2.2.</span> <span class="nav-text">均方损失（MSE）</span></a></li></ol></li></ol></div>
            

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